FluNet: An AI-Enabled Influenza-Like Warning System

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dc.contributor.authorWard, Ryan J.
dc.contributor.authorJjunju, Fred Paul Mark
dc.contributor.authorKabenge, Isa
dc.contributor.authorWanyenze, Rhoda
dc.contributor.authorGriffith, Elias J.
dc.contributor.authorBanadda, Noble
dc.contributor.authorTaylor, Stephen
dc.contributor.authorMarshall, Alan
dc.date.accessioned2022-11-01T11:32:21Z
dc.date.available2022-11-01T11:32:21Z
dc.date.issued2021
dc.description.abstractInfluenza is an acute viral respiratory disease that is currently causing severe financial and resource strains worldwide. With the COVID-19 pandemic exceeding 153 million cases worldwide, there is a need for a low-cost and contactless surveillance system to detect symptomatic individuals. The objective of this study was to develop FluNet, a novel, proof-of-concept, low-cost and contactless device for the detection of high-risk individuals. The system conducts face detection in the LWIR with a precision rating of 0.98, a recall of 0.91, an F-score of 0.96, and a mean intersection over union of 0.74 while sequentially taking the temperature trend of faces with a thermal accuracy of ± 1 K. In parallel, determining if someone is coughing by using a custom lightweight deep convolutional neural network with a precision rating of 0.95, a recall of 0.92, an F-score of 0.94 and an AUC of 0.98. We concluded this study by testing the accuracy of the direction of arrival estimation for the cough detection revealing an error of ± 4.78 . If a subject is symptomatic, a photo is taken with a specified region of interest using a visible light camera. Two datasets have been constructed, one for face detection in the LWIR consisting of 250 images of 20 participants’ faces at various rotations and coverings, including face masks. The other for the real-time detection of coughs comprised of 40,482 cough / not cough sounds. These findings could be helpful for future low-cost edge computing applications for influenza-like monitoring.en_US
dc.identifier.citationWard, R. J., Jjunju, F. P. M., Kabenge, I., Wanyenze, R., Griffith, E. J., Banadda, N., ... & Marshall, A. (2021). FluNet: An AI-Enabled Influenza-Like Warning System. IEEE sensors journal, 21(21), 24740-24748. https://doi.org/10.1109/JSEN.2021.3113467en_US
dc.identifier.urihttps://doi.org/10.1109/JSEN.2021.3113467
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/5103
dc.language.isoenen_US
dc.publisherIEEE sensors journalen_US
dc.subjectCough detectionen_US
dc.subjectCOVIDen_US
dc.subjectCOVID-19en_US
dc.subjectSARSen_US
dc.subjectFace detectionen_US
dc.subjectMachine learningen_US
dc.titleFluNet: An AI-Enabled Influenza-Like Warning Systemen_US
dc.typeArticleen_US
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